Hybrid Satellite-Terrestrial Communication Networks for the Maritime Internet of Things: Key Technologies, Opportunities, and Challenges

With the rapid development of marine activities, there has been an increasing number of maritime mobile terminals, as well as a growing demand for high-speed and ultra-reliable maritime communications to keep them connected. Traditionally, the maritime Internet of Things (IoT) is enabled by maritime satellites. However, satellites are seriously restricted by their high latency and relatively low data rate. As an alternative, shore & island-based base stations (BSs) can be built to extend the coverage of terrestrial networks using fourth-generation (4G), fifth-generation (5G), and beyond 5G services. Unmanned aerial vehicles can also be exploited to serve as aerial maritime BSs. Despite of all these approaches, there are still open issues for an efficient maritime communication network (MCN). For example, due to the complicated electromagnetic propagation environment, the limited geometrically available BS sites, and rigorous service demands from mission-critical applications, conventional communication and networking theories and methods should be tailored for maritime scenarios. Towards this end, we provide a survey on the demand for maritime communications, the state-of-the-art MCNs, and key technologies for enhancing transmission efficiency, extending network coverage, and provisioning maritime-specific services. Future challenges in developing an environment-aware, service-driven, and integrated satellite-air-ground MCN to be smart enough to utilize external auxiliary information, e.g., sea state and atmosphere conditions, are also discussed

Dynamics in Logistics

This open access book highlights the interdisciplinary aspects of logistics research. Featuring empirical, methodological, and practice-oriented articles, it addresses the modelling, planning, optimization and control of processes. Chiefly focusing on supply chains, logistics networks, production systems, and systems and facilities for material flows, the respective contributions combine research on classical supply chain management, digitalized business processes, production engineering, electrical engineering, computer science and mathematical optimization. To celebrate 25 years of interdisciplinary and collaborative research conducted at the Bremen Research Cluster for Dynamics in Logistics (LogDynamics), in this book hand-picked experts currently or formerly affiliated with the Cluster provide retrospectives, present cutting-edge research, and outline future research directions

Framework and Methodology for Establishing Port-City Policies Based on Real-Time Composite Indicators and IoT: A Practical Use-Case

[EN] During the past few decades, the combination of flourishing maritime commerce and urban population increases has made port-cities face several challenges. Smart Port-Cities of the future will take advantage of the newest IoT technologies to tackle those challenges in a joint fashion from both the city and port side. A specific matter of interest in this work is how to obtain reliable, measurable indicators to establish port-city policies for mutual benefit. This paper proposes an IoTbased software framework, accompanied with a methodology for defining, calculating, and predicting composite indicators that represent real-world phenomena in the context of a Smart PortCity. This paper envisions, develops, and deploys the framework on a real use-case as a practice experiment. The experiment consists of deploying a composite index for monitoring traffic congestion at the port-city interface in Thessaloniki (Greece). Results were aligned with the expectations, validated through nine scenarios, concluding with delivery of a useful tool for interested actors at Smart Port-Cities to work over and build policies upon.This research was funded, by the European Commission, via the agency INEA, under the H2020-project PIXEL, grant number 769355, and, when applicable, by the H2020-project DataPorts, grant number 871493, via the DG-CONNECT agency.Lacalle, I.; Belsa, A.; Vaño, R.; Palau Salvador, CE. (2020). Framework and Methodology for Establishing Port-City Policies Based on Real-Time Composite Indicators and IoT: A Practical Use-Case. Sensors. 20(15):1-41. https://doi.org/10.3390/s20154131S1412015Urban Population Growthhttps://www.who.int/gho/urban_health/situation_trends/urban_population_growth_text/en/Smart Port Cityhttps://maritimestreet.fr/smart-port-city/The World’s 33 Megacitieshttps://www.msn.com/en-us/money/realestate/the-worlds-33-megacities/ar-BBUaR3vDocksTheFuture Project Maritime Traffic Analysis and Forecast Review-Key Resultshttps://www.docksthefuture.eu/wp-content/uploads/2020/04/Attachment_0-2019-09-09T135818.886-1.pdfHamburg Port Authority: SmartPORThttps://www.hamburg-port-authority.de/en/hpa-360/smartport/Guo, H., Wang, L., Chen, F., & Liang, D. (2014). Scientific big data and Digital Earth. Chinese Science Bulletin, 59(35), 5066-5073. doi:10.1007/s11434-014-0645-3AIVP Agenda 2030 for Sustainable Port-Citieshttps://www.aivpagenda2030.com/Urban Transport Challengeshttps://transportgeography.org/?page_id=4621Passenger Cars in the EUhttps://ec.europa.eu/eurostat/statistics-explained/index.php/Passenger_cars_in_the_EUAverage CO2 Emissions from New Cars and Vans Registered in Europe Increased in 2018, Requiring Significant Emission Reductions to Meet the 2020 Targetshttps://ec.europa.eu/clima/news/average-co2-emissions-new-cars-and-vans-registered-europe-increased-2018-requiring-significant_en7 Smart City Solutions to Reduce Traffic Congestionhttps://www.geotab.com/blog/reduce-traffic-congestion/The Port and the City—Thoughts on the Relation between Cities and Portshttps://theportandthecity.wordpress.com/Yau, K.-L. A., Peng, S., Qadir, J., Low, Y.-C., & Ling, M. H. (2020). Towards Smart Port Infrastructures: Enhancing Port Activities Using Information and Communications Technology. IEEE Access, 8, 83387-83404. doi:10.1109/access.2020.2990961Two Projects Led by Valenciaport Win the IAPH World Port Sustainability Awards 2020—Valenciaporthttps://www.valenciaport.com/en/two-projects-led-by-valenciaport-win-the-iaph-world-port-sustainability-awards-2020/Ahlgren, B., Hidell, M., & Ngai, E. C.-H. (2016). Internet of Things for Smart Cities: Interoperability and Open Data. IEEE Internet Computing, 20(6), 52-56. doi:10.1109/mic.2016.124Inkinen, T., Helminen, R., & Saarikoski, J. (2019). Port Digitalization with Open Data: Challenges, Opportunities, and Integrations. Journal of Open Innovation: Technology, Market, and Complexity, 5(2), 30. doi:10.3390/joitmc5020030Analytical Report 4: Open Datain Citieshttps://www.europeandataportal.eu/sites/default/files/edp_analytical_report_n4_-_open_data_in_cities_v1.0_final.pdfAnalytical Report 6: Open Datain Cities 2https://www.europeandataportal.eu/sites/default/files/edp_analytical_report_n6_-_open_data_in_cities_2_-_final-clean.pdfINTER-IoT Deliverableshttps://inter-iot.eu/deliverablesActivage Project D3.1 Report on IoT European Platformshttps://www.activageproject.eu/docs/downloads/activage_public_deliverables/ACTIVAGE_D3.1_M3_ReportonIoTEuropeanPlatforms_V1.0.pdfThe Open Source Platform for Our Smart Digital Future—FIWAREhttps://www.fiware.org/FIWARE Data Modelshttps://fiware-datamodels.readthedocs.io/en/latest/index.htmlApache Kafkahttps://kafka.apache.org/FIWARE Orion Context Brokerhttps://fiware-orion.readthedocs.io/en/master/Saborido, R., & Alba, E. (2020). Software systems from smart city vendors. Cities, 101, 102690. doi:10.1016/j.cities.2020.102690Santana, E. F. Z., Chaves, A. P., Gerosa, M. A., Kon, F., & Milojicic, D. S. (2018). Software Platforms for Smart Cities. ACM Computing Surveys, 50(6), 1-37. doi:10.1145/3124391Smart Citieshttps://www.fiware.org/community/smart-cities/Araujo, V., Mitra, K., Saguna, S., & Åhlund, C. (2019). Performance evaluation of FIWARE: A cloud-based IoT platform for smart cities. Journal of Parallel and Distributed Computing, 132, 250-261. doi:10.1016/j.jpdc.2018.12.010Ismagilova, E., Hughes, L., Dwivedi, Y. K., & Raman, K. R. (2019). Smart cities: Advances in research—An information systems perspective. International Journal of Information Management, 47, 88-100. doi:10.1016/j.ijinfomgt.2019.01.004Albino, V., Berardi, U., & Dangelico, R. M. (2015). Smart Cities: Definitions, Dimensions, Performance, and Initiatives. Journal of Urban Technology, 22(1), 3-21. doi:10.1080/10630732.2014.942092Alavi, A. H., Jiao, P., Buttlar, W. G., & Lajnef, N. (2018). Internet of Things-enabled smart cities: State-of-the-art and future trends. Measurement, 129, 589-606. doi:10.1016/j.measurement.2018.07.067Samih, H. (2019). Smart cities and internet of things. Journal of Information Technology Case and Application Research, 21(1), 3-12. doi:10.1080/15228053.2019.1587572Lanza, J., Sánchez, L., Gutiérrez, V., Galache, J., Santana, J., Sotres, P., & Muñoz, L. (2016). Smart City Services over a Future Internet Platform Based on Internet of Things and Cloud: The Smart Parking Case. Energies, 9(9), 719. doi:10.3390/en9090719A Novel Architecture for Modelling, Virtualising and Managing the Energy Consumption of Household Appliances|AIM Project|FP7|CORDIS|European Commissionhttps://cordis.europa.eu/project/id/224621Intelligent Use of Buildings’ Energy Information|IntUBE Project|FP7|CORDIS|European Commissionhttps://cordis.europa.eu/project/id/224286Scuotto, V., Ferraris, A., & Bresciani, S. (2016). Internet of Things: applications and challenges in smart cities. A case study of IBM smart city projects. Business Process Management Journal, 22(2). doi:10.1108/bpmj-05-2015-0074Molavi, A., Lim, G. J., & Race, B. (2019). A framework for building a smart port and smart port index. International Journal of Sustainable Transportation, 14(9), 686-700. doi:10.1080/15568318.2019.1610919Moustaka, V., Vakali, A., & Anthopoulos, L. G. (2019). A Systematic Review for Smart City Data Analytics. ACM Computing Surveys, 51(5), 1-41. doi:10.1145/3239566Alam, M., Dupras, J., & Messier, C. (2016). A framework towards a composite indicator for urban ecosystem services. Ecological Indicators, 60, 38-44. doi:10.1016/j.ecolind.2015.05.035PIXEL Project D5.1 Environmental Factors and Mapping to Pilotshttps://pixel-ports.eu/wp-content/uploads/2020/05/D5.1-Environmental-aspects-and-mapping-to-pilots.pdfEconomic Sentiment Indicator—Eurostathttps://ec.europa.eu/eurostat/web/products-datasets/product?code=teibs010Human Development Index (HDI)|Human Development Reportshttp://hdr.undp.org/en/content/human-development-index-hdiCOIN|Competence Centre on Composite Indicators and Scoreboardshttps://composite-indicators.jrc.ec.europa.eu/CITYkeys Projecthttp://www.citykeys-project.eu/citykeys/homeCITYkeys D1-4 Indicators for Smart City Projects and Smart Citieshttp://nws.eurocities.eu/MediaShell/media/CITYkeysD14Indicatorsforsmartcityprojectsandsmartcities.pdfMake Healthy Choices Easier Options—Scientific Americanhttps://www.scientificamerican.com/podcast/episode/make-healthy-choices-easier-options-12-09-20/FIWARE E Interoperabilidad Para Smart Citieshttps://www.apegr.org/images/descargas/J7OctESMARTCITY/2PresentacionFIWARE.pdfChen, G., Govindan, K., & Yang, Z. (2013). Managing truck arrivals with time windows to alleviate gate congestion at container terminals. International Journal of Production Economics, 141(1), 179-188. doi:10.1016/j.ijpe.2012.03.033Patel, N., & Mukherjee, A. B. (2015). Assessment of network traffic congestion through Traffic Congestability Value (TCV): a new index. Bulletin of Geography. Socio-economic Series, 30(30), 123-134. doi:10.1515/bog-2015-0039Aimsun Live: Model Every Movement at Every Momenthttps://www.aimsun.com/aimsun-live/PTV Vissim: Traffic Simulation Softwarehttps://www.ptvgroup.com/en/solutions/products/ptv-vissim/IBM Traffic Prediction Toolhttps://researcher.watson.ibm.com/researcher/view_group_subpage.php?id=1248Veins: The Open Source Vehicular Network Simulation Frameworkhttps://veins.car2x.org/Mena-Yedra, R., Gavaldà, R., & Casas, J. (2017). Adarules: Learning rules for real-time road-traffic prediction. Transportation Research Procedia, 27, 11-18. doi:10.1016/j.trpro.2017.12.106PIXEL Projecthttps://pixel-ports.euReference Architectural Model Industrie 4.0 (rami 4.0)https://www.plattform-i40.de/PI40/Navigation/EN/Home/home.htmlSethi, P., & Sarangi, S. R. (2017). Internet of Things: Architectures, Protocols, and Applications. Journal of Electrical and Computer Engineering, 2017, 1-25. doi:10.1155/2017/9324035Containers & Containerization—The Pros and Conshttps://spin.atomicobject.com/2019/05/24/containerization-pros-cons/Pyngsi Frameworkhttps://github.com/pixel-ports/pyngsiPIXEL Project D6.2 PIXEL Information System Architecture and Design—Version 2https://pixel-ports.eu/wp-content/uploads/2020/05/D6.2-PIXEL-Information-System-architecture-and-design-v2.pdfApache Hivehttps://hive.apache.org/MySQLhttps://www.mysql.com/MariaDB Serverhttps://mariadb.org/Elasticsearchhttps://www.elastic.co/elasticsearch/MongoDBhttps://www.mongodb.com/Node-REDhttps://nodered.org/Swarm Mode Overview | Docker Documentationhttps://docs.docker.com/engine/swarm/Kuberneteshttps://kubernetes.io/PIXEL Project D6.3 PIXEL Data Acquisition, Information Hub and Data Representation v1https://pixel-ports.eu/wp-content/uploads/2020/05/D6.3_PIXEL-data-acquisition-information-hub-and-data-representation-v1.pdfOverview of Docker Compose|Docker Documentationhttps://docs.docker.com/compose/Kibana: Explore, Visualize, Discover Datahttps://www.elastic.co/kibanaGrafana: The Open Observability Platformshttps://grafana.com/Vue.jshttps://vuejs.org/PIXEL Project D5.2 PEI Definition and Algorithms v1https://pixel-ports.eu/wp-content/uploads/2020/05/D5.2-PEI-Definition-and-Algorithms-v1.pdfKeyPerformanceIndicator—FIWARE Data Modelshttps://fiware-datamodels.readthedocs.io/en/latest/KeyPerformanceIndicator/doc/spec/index.htmlWhat Is a Container?|App Containerization|Dockerhttps://www.docker.com/resources/what-containerGarcia-Alonso, L., Moura, T. G. Z., & Roibas, D. (2020). The effect of weather conditions on port technical efficiency. Marine Policy, 113, 103816. doi:10.1016/j.marpol.2020.103816TrafficThess—LIVE Traffic in Thessaloniki, Greecehttps://www.trafficthess.imet.gr/National Observatory of Athens—Meteo—Stations’ Live Data and Databasehttp://stratus.meteo.noa.gr/frontHow to Use Smart Data Models in Your Projects—FIWARE Data Modelshttps://fiware-datamodels.readthedocs.io/en/latest/howto/index.htmlGan, X., Fernandez, I. C., Guo, J., Wilson, M., Zhao, Y., Zhou, B., & Wu, J. (2017). When to use what: Methods for weighting and aggregating sustainability indicators. Ecological Indicators, 81, 491-502. doi:10.1016/j.ecolind.2017.05.068Wilson, M. C., & Wu, J. (2016). The problems of weak sustainability and associated indicators. International Journal of Sustainable Development & World Ecology, 24(1), 44-51. doi:10.1080/13504509.2015.1136360Kumar, S. V., & Vanajakshi, L. (2015). Short-term traffic flow prediction using seasonal ARIMA model with limited input data. European Transport Research Review, 7(3). doi:10.1007/s12544-015-0170-8Prophet: Forecastig at Scalehttps://facebook.github.io/prophet/PIXEL Project D4.4 PredictiveAlgorithms v2https://pixel-ports.eu/wp-content/uploads/2020/05/PIXEL_D4.4_Predictive-Algorithms_v2.0_Final.pdfProject Jupyterhttps://jupyter.org/FIWARE Cygnushttps://fiware-cygnus.readthedocs.io/en/latest/NGSIElasticsearchSink—FIWARE Cygnushttps://fiware-cygnus.readthedocs.io/en/latest/cygnus-ngsi/flume_extensions_catalogue/ngsi_elasticsearch_sink/index.htmlNode.jshttps://nodejs.org/Elasticsearch Node.js Client [7.x]https://www.elastic.co/guide/en/elasticsearch/client/javascript-api/current/index.htmlApache HTTP Server Projecthttps://httpd.apache.org/Everything You Need to Know about Min-Max Normalization: A Python Tutorialhttps://towardsdatascience.com/everything-you-need-to-know-about-min-max-normalization-in-python-b79592732b79OpenStreetMaphttps://www.openstreetmap.org/Leaflet—A JavaScript Library for Interactive Mapshttps://leafletjs.com/AmCharts: JavaScript Charts & Mapshttps://www.amcharts.com/FIWARE Cataloguehttps://www.fiware.org/developers/catalogue/Findlow, S. (2019). ‘Citizenship’ and ‘Democracy Education’: identity politics or enlightened political participation? British Journal of Sociology of Education, 40(7), 1004-1013. doi:10.1080/01425692.2019.1656910Mouradian, C., Naboulsi, D., Yangui, S., Glitho, R. H., Morrow, M. J., & Polakos, P. A. (2018). A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges. IEEE Communications Surveys & Tutorials, 20(1), 416-464. doi:10.1109/comst.2017.277115

Dynamics in Logistics

This open access book highlights the interdisciplinary aspects of logistics research. Featuring empirical, methodological, and practice-oriented articles, it addresses the modelling, planning, optimization and control of processes. Chiefly focusing on supply chains, logistics networks, production systems, and systems and facilities for material flows, the respective contributions combine research on classical supply chain management, digitalized business processes, production engineering, electrical engineering, computer science and mathematical optimization. To celebrate 25 years of interdisciplinary and collaborative research conducted at the Bremen Research Cluster for Dynamics in Logistics (LogDynamics), in this book hand-picked experts currently or formerly affiliated with the Cluster provide retrospectives, present cutting-edge research, and outline future research directions

The Development of a Service Oriented Architecture for Digital Services on Maritime Vessels.

Mechanical and Mechatronic Engineerin

Annual General Assembly of the International Association of Maritime Universities

978-84-947311-7-